S
Skanda Koppula
Researcher at Google
Publications - 24
Citations - 311
Skanda Koppula is an academic researcher from Google. The author has contributed to research in topics: Computer science & Overhead (computing). The author has an hindex of 7, co-authored 14 publications receiving 165 citations. Previous affiliations of Skanda Koppula include Massachusetts Institute of Technology & Harvard University.
Papers
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Proceedings ArticleDOI
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM
Skanda Koppula,Lois Orosa,A. Giray Yaglikci,Roknoddin Azizi,Taha Shahroodi,Konstantinos Kanellopoulos,Onur Mutlu +6 more
TL;DR: EDEN is the first general framework that reduces DNN energy consumption and DNN evaluation latency by using approximate DRAM devices, while strictly meeting a user-specified target DNN accuracy, and reliably improves the error resiliency of the DNN by an order of magnitude.
Proceedings ArticleDOI
SMASH: Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations
Konstantinos Kanellopoulos,Nandita Vijaykumar,Christina Giannoula,Roknoddin Azizi,Skanda Koppula,Nika Mansouri Ghiasi,Taha Shahroodi,Juan Gómez Luna,Onur Mutlu +8 more
TL;DR: In this article, the authors propose a hardware-software cooperative mechanism that enables highly efficient indexing and storage of sparse matrices by explicitly enabling the hardware to recognize and exploit sparsity in data.
Proceedings ArticleDOI
SMASH: Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations
Konstantinos Kanellopoulos,Nandita Vijaykumar,Christina Giannoula,Roknoddin Azizi,Skanda Koppula,Nika Mansouri Ghiasi,Taha Shahroodi,Juan Gómez Luna,Onur Mutlu +8 more
TL;DR: This paper proposes SMASH, a hardware-software cooperative mechanism that enables highly-efficient indexing and storage of sparse matrices and devise a novel software encoding based on a hierarchy of bitmaps that can be used to efficiently compress any sparse matrix, regardless of the extent and structure of sparsity.
Proceedings ArticleDOI
Object discovery and representation networks
Olivier J. H'enaff,Skanda Koppula,Evan Shelhamer,Deb Zoran,Andrew Jaegle,A. Zisserman,Joao Carreira,Relja Arandjelovic +7 more
TL;DR: Odin is proposed, a self-supervised learning paradigm that discovers meaningful image segmentations without any supervision and achieves state-of-the-art transfer learning results for object detection and instance segmentation on COCO, and semantic segmentsation on PASCAL and Cityscapes, while strongly surpassing supervised pre-training for video segmentations on DAVIS.
Posted Content
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM.
Skanda Koppula,Lois Orosa,Abdullah Giray Yağlıkçı,Roknoddin Azizi,Taha Shahroodi,Konstantinos Kanellopoulos,Onur Mutlu +6 more
TL;DR: EDEN as mentioned in this paper proposes a general framework that reduces DNN energy consumption and DNN evaluation latency by using approximate DRAM devices, while strictly meeting a user-specified target DNN accuracy.